© EZ-R Stats, LLC Duplicate Payments Slide 1 Auditing for Duplicate Payments A better way … Web...
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Transcript of © EZ-R Stats, LLC Duplicate Payments Slide 1 Auditing for Duplicate Payments A better way … Web...
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 2
About duplicate payments Why they occur Fraud Errors Control breakdowns
System Procedures
How to detect
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 3
Historical ExperienceState of North Carolina
Fiscal 1996 – 2004 $4.5 million recovered Approximately $500K /
year Most recent experience
About $400K/ year
“Pay and Chase”
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 4
Matching approach
Exact matching“Fuzzy”
matchingEvery possible
pair
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 6
Why check for them?Recovery feePossibility of
fraudIdentify
control break downs
Proactive checking
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 7
Cost Recoveries
“Pay and Chase”35% fee to Cost Recovery ContractorRisk of loss
Proactive Approach Identify up-frontMake control recommendations to
preventContinuous monitoring
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 8
Invoice elements Vendor number Invoice number Invoice Date Invoice Amount
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 9
Exact matchesAll four elements matchThree combinations of three way
matchVendor, invoice, amountVendor, invoice, dateVendor, amount, date
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 10
“Fuzzy” matches – invoice numbers
Levenshtein distance
Transpositions LDO (letters, digits
only) Same characters Leading characters Trailing characters
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 11
Example of Levenshtein distance
Measure similarity of invoice “12341” and “24371”.
Start 12341 24371
Step1 – delete left most digit
2341 24371
Step2 – Insert a “4” between “2” and “3”
24341 24371
Step 3- Replace seond “4” with “7”
24371 24371
Levenshtein distance is 3
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 13
Date Transpositions07/31/2010 vs. 07/13/2010
01/21/2009 vs. 02/11/2009
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 14
Data validation
Invoice date Invoice amount Vendor number
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 17
Forms
Browser based Pull down menus “Fill in the blanks”
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 19
Processing volumes500,000 invoices40,000 vendorsProcess on lap-top with dual
2.2 GHzAbout two minutes per test
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 24
19 Tests can be run “A” – “S”
Description of tests Used for identifying potential duplicate
payments Same concept applies to other areas
Journal entries Purchase orders Expense reports Fixed asset items, etc.
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 25
Test “A”
All four key values equal Same vendor Same amount Same invoice date Same invoice number Note: case insensitive comparisons
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 26
Test “B”
Same vendor,Same invoice number,Same invoice amount
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 27
Test “C”
Same vendor number,Same invoice number,Same invoice date
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 28
Test “D”
Same vendor,Same invoice amount,Same invoice date
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 29
Test “E”
Same vendor number,Same invoice amount,Two invoice numbers the same
considering letters and digits only (i.e. no special characters)
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 30
Test “F “
Same vendor,Same invoice amount,Same invoice number, if only
letters are considered
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 31
Test “G”
Same vendor number,Same invoice amount,Same invoice number, if only
digits are considered (i.e. ignore letters and special characters, blanks, etc.)
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 32
Test “H”
Same vendor,Same invoice amount,Invoice numbers are within the
specified Levenshtein distance
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 33
Test “I”
Same vendor,Same invoice amount,Invoice numbers are different
due only to a transposition
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 34
Test “J”
Same vendor number, Same invoice amount, Over 90% of the characters/digits in
each invoice are the same Can specify different percentage Characters not necessarily in same
sequence
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 35
Test “K”
Same vendor,Same invoice date,Invoice amounts are within 2%
of each otherCan specify different
percentage
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 36
Test “L”
Same vendor, Same invoice date, First four leading characters of two
invoices are the same Can use different number of leading
digits Can specify different tests (LDO, DO,
LO)
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 37
Test “M”
Same vendor, Same invoice date, First four trailing characters of two
invoices are the same Can use different number of leading
digits Can specify different tests (LDO, DO,
LO)
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 38
Test “N”
Same vendor,Same invoice date,Same invoice amountDifferent invoices
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 39
Test “O”
Same invoice number,Same invoice date,Same invoice amount,Different vendor
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 40
Test “P”
Same invoice number,Same invoice date,Similar amountMeasure as percentageAbs(invamt1-invamt2)/invamt1Auditor specifies percentage, e.g.
2%
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 41
Test “Q”
Same vendor, Same invoice amount, Same invoice number Similar invoice date Measured using Levenshtein distance Auditor specifies test distance
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 42
Test “R”
Same vendor,Same invoice number,Similar dateMeasured using Levenshtein
distanceAuditor specifies distance
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 43
Test “S”
Same invoice number, Same invoice date, Same invoice amount Similar vendor number Measured using Levenshtein distance Auditor specifies distance
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 44
Output
Output is to a text fileImport into ExcelPairs of rows
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 45
Limitations
Currently handles only: Excel Access Text files (csv,tsv, etc.) No limit on rows (other than imposed by Excel) Has been tested using about 450,000 invoices Feasible to run on PC
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 46
Processing times
File of 10,000 payments takes less than one minute
Some tests take longer:Levenshtein distanceLeading/trailing digits
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 47
Benchmark results500,000 invoices tested6,000 vendorsDone on lap-top with dual 2.2
GHzAbout two minutes per testLarger volumes require longerYMMV
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 48
Duplicate Vendors
Primary cause of duplicate payments Identified using two primary methods
Exact – Same, same, different “Fuzzy” – Name matching
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 49
Same, Same, Different
Same IRS Taxpayer ID (TIN) Different Vendor Number
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 50
Same, Same, Different
Same Street Address Same City Same Zip Code Different Vendor Number
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 51
Same, Same, Different
Same area code, Same contact number Same contact name Different Vendor Name
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 52
Same, Same, Different
Same bank routing numberDifferent vendor name/number
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 53
“Fuzzy” matching of vendor names
Remove common terms (e.g. “corp”, “inc” etc.)
Remove all but letters and numbers Compare every combination using-
Match after removal of special characters Leading “N” characters Levenshtein distance
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 54
Fuzzy matching of TIN
TranspositionsLevenshtein distance
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 55
Fuzzy matching of Bank routing number Transpositions Levenshtein distance
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 56
Fuzzy matching of address
Letters and digits only Levenshtein distance Transpositions Same characters
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 57
Benchmark Timings
10,000 vendors Access database CPU 1.5 GHz, memory 500MB Same, same, different - < 1 minute “Fuzzy”
LDO – 20 seconds Leading – 2 minutes 10 seconds Levenshtein - ? (long time)
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 58
Continuous Monitoring
ObjectivesIdentify issues earlyVerify controls are workingQuantify areas for audit
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 59
Monitor for potential duplicate payments
Set up “duplicate payment test” directory Designate “log” file Run / refine tests Convert log file to “monitor” file Now simple to run tests on a cycle Just update file containing payments
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 60
Monitoring process
Run all testsReview outputReview for errors in current periodIdentify potential overpayments
early
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 61
Other areas for monitoring
Journal entries Expense reports P-card transactions Vendor payment trends Payroll Inventory Receivables Vendor master file
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 62
Example monitor processes
Invoice payments – regression analysis Counts Totals Averages By month, week,
quarter, etc. Policy compliance
Requirements for PO
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 63
Monitoring (cont’d)
Use of Benford’s Law Identification of credits
not taken Top “10” Discounts not taken Vendor master –
Checking for duplicates
Checking for PO Boxes/ drop boxes
Employee conflict of interest
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 64
Monitoring (cont’d)
“Impossible” transaction conditions
Data stratification Population statistics Quartiles Duplicate transactions Sequence gaps
Same, same, different
May 29, 2010 © 2010 EZ-R Stats, LLC Slide 65
More info
Auditors Guide to MonitoringUser Guide – Audit Comman
der